Classification Models for Cloud-Based Distributed Denial of Service (DDoS) Attack Detection
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The rapid proliferation of internet-connected devices and cloud services,combined with increasing bandwidth capabilities and the widespread accessibility of DDoS tools, has significantly expanded the threat landscape. The complexity and sophistication of DDoS attacks posture detection and mitigation challenges (Alhijawi et al., 2022). Additionally, the monetary losses due to DDoS attacks can be insurmountable. For example. “downtime due to a DDoS attack costs organizations an average of $6,130 per minute” (Radware, 2023, P. 29). This praxis introduces a number of Cloud-Based DDoS detection models for modern cloud and distributed attacks. The modeling methodology leverages a dataset of real and modern attack scenarios. A collection of DDoS detection models was developed with improved model metrics, including innovative modeling, data exploration, and feature selection approaches.
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